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2024/25 Taught Postgraduate Module Catalogue

COMP5611M Machine Learning

15 creditsClass Size: 300

Module manager: Dr Yanlong Huang
Email: Y.L.Huang@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Objectives

On completion of this module, students should be able to:
• list the principal algorithms used in machine learning, and derive their update rules
• appreciate the capabilities and limitations of current approaches;
• evaluate the performance of machine learning algorithms;
• use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Syllabus

Topics selected from:
Neural networks, decision trees, support vector machines, Bayesian learning, instance-based learning, linear regression, clustering, reinforcement learning, deep learning.
Methods for evaluating performance.
Examples will be drawn from simple problems that arise in studies of robotics and computer vision.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture221.0022.00
Practical102.0020.00
Private study hours108.00
Total Contact hours42.00
Total hours (100hr per 10 credits)150.00

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
PracticalProgramming Project40.00
Total percentage (Assessment Coursework)40.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated


Exams
Exam typeExam duration% of formal assessment
Open Book exam2 hr 00 mins60.00
Total percentage (Assessment Exams)60.00

This module will be reassessed by open book examination.

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:12:35

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